This track focuses on innovative predictive modeling techniques utilizing cloud-based platforms. Researchers are invited to present methodologies that enhance the accuracy and efficiency of predictive analytics in engineering applications.

This session explores the application of big data analytics in various engineering domains. Contributions should highlight novel approaches and case studies that demonstrate the impact of big data on engineering decision-making.

This track examines the use of supervised and unsupervised learning techniques in engineering contexts. Papers should discuss the effectiveness of these methods in solving real-world engineering problems.

This session is dedicated to the advancements in deep learning technologies applicable to engineering. Submissions should focus on new architectures, algorithms, and applications that address complex engineering challenges.

This track investigates methods for feature extraction and anomaly detection within cloud-based data science platforms. Presentations should emphasize techniques that improve the identification of outliers in engineering datasets.

This session highlights the role of distributed computing in optimizing data processing workflows. Contributions should explore how distributed systems can be leveraged to handle large-scale engineering data efficiently.